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In this paper, we present a new approach to NIDS deployment based on machine learning. This new approach is based on detecting attackers by analyzing the relationship between computers over time. The basic idea that we rely on is that the behaviors of attackers’ computers are different from those of other computers, because the timings and durations of their connections are different and therefore easy to detect. This approach does not analyze each network packet statistically. It analyzes, over a period of time, all traffic to obtain temporal behaviors and to determine if the IP is an attacker instead of that packet. IP behavior analysis reduces drastically the number of alerts generated. Our approach collects all interactions between computers, transforms them into time series, classifies them, and assembles them into a complex temporal behavioral network. This process results in the complex characteristics of each computer that allow us to detect which are the attackers’ addresses. To reduce the computational efforts of previous approaches, we propose to use visibility graphs instead of other time series classification methods, based on signal processing techniques. This new approach, in contrast to previous approaches, uses visibility graphs and reduces the computational time for time series classification. However, the accuracy of the model is maintained.

The main aim of the present study was to explore the value of several measures of handwriting in the study of motor abnormalities in patients with bipolar or psychotic disorders. 54 adult participants with a schizophrenia spectrum disorder or bipolar disorder and 44 matched healthy controls, participated in the study. Participants were asked to copy a handwriting pattern consisting of four loops, with an inking pen on a digitizing tablet. We collected a number of classical, non-linear and geometrical measures of handwriting. The handwriting of patients was characterized by a significant decrease in velocity and acceleration and an increase in the length, disfluency and pressure with respect to controls. Concerning non-linear measures, we found significant differences between patients and controls in the Sample Entropy of velocity and pressure, Lempel-Ziv of velocity and pressure, and Higuchi Fractal Dimension of pressure. Finally, Lacunarity, a measure of geometrical heterogeneity, was significantly greater in handwriting patterns from patients than from controls. We did not find differences in any handwriting measure on function of the specific diagnosis or the antipsychotic dose. Results indicate that participants with a schizophrenia spectrum disorder or bipolar disorder exhibit significant motor impairments and that these impairments can be readily quantified using measures of handwriting movements. Besides, they suggest that motor abnormalities are a core feature of several mental disorders and they seem to be unrelated to the pharmacological treatment.

This paper outlined the advantages of the k-visibility algorithm proposed in [1 ,2 ] compared to traditional time series clustering algorithms, highlighting enhanced computational efficiency and comparable clustering quality. This method leveraged visibility graphs, transforming time series into graph structures where data points were represented as nodes, and edges are established based on visibility criteria. It employed the traditional k-means clustering method to cluster the time series. This approach was particularly efficient for long time series and demonstrated superior performance compared to existing clustering methods. The structural properties of visibility graphs provided a robust foundation for clustering, effectively capturing both local and global patterns within the data. In this paper, we have compared the k-visibility algorithm with 4 algorithms frequently used in time series clustering and compared the results in terms of accuracy and computational time. To validate the results, we have selected 15 datasets from the prestigious UCR (University of California, Riverside) archive in order to make a homogeneous validation. The result of this comparison concluded that k-visibility was always the fastest algorithm and that it was one of the most accurate in matching the clustering proposed by the UCR archive.

Psychotic syndromes, such as schizophrenia (SCZ) and bipolar disorder (BD), significantly disrupt brain electrical activity, with functional connectivity (FC) being particularly affected. However, FC is often estimated as a static measure, overlooking the brain dynamic fluctuations that naturally occur, even at rest. In this study, we investigated alterations in dynamic FC (dFC) using resting-state electroencephalographic (EEG) data from SCZ patients, BD patients, and age-matched healthy control (HC) subjects. To achieve this, the instantaneous amplitude correlation (IAC) was computed for each EEG recording within the canonical frequency bands. We then analyzed the first- to fourth-order cumulants of the average strength (aS) time series derived from the IAC matrices. Statistically significant differences were obtained between the SCZ and HC groups in aS mean (first-order cumulant) and aS skewness (third-order cumulant) at the gamma band, while the BD group reported differences against the HC group in aS mean at the delta band. Additionally, both disorders exhibited altered aS skewness in the beta band; these findings suggest disruptions in interneuronal communication, manifesting as “pathologically Gaussian” aS distributions over time. Our results highlight the potential of dFC analysis to uncover brain function anomalies that remain undetected with conventional approaches.

Schizophrenia (SZ) is a complex disorder characterized by a range of symptoms and behaviors that have significant consequences for individuals, families, and society in general. Electroencephalography (EEG) is a valuable tool for understanding the neural dynamics and functional abnormalities associated with schizophrenia. Research studies utilizing EEG have identified specific patterns of brain activity in individuals diagnosed with schizophrenia that may reflect disturbances in neural synchronization and information processing in cortical circuits. Considering the temporal dynamics of functional connectivity provides a more comprehensive understanding of brain networks’ organization and how they change during different cognitive states. This temporal perspective would enhance our understanding of the underlying mechanisms of schizophrenia. In the present study, we will use measures based on graph theory to obtain dynamic and static indicators in order to evaluate differences in the functional connectivity of individuals diagnosed with SZ and healthy controls using an ecologically valid task. At the static level, patients showed alterations in their ability to segregate information, particularly in the default mode network (DMN). As for dynamic measures, patients showed reduced values in most metrics (segregation, integration, centrality, and resilience), reflecting a reduced number of dynamic states of brain networks. Our results show the utility of combining static and dynamic indicators of functional connectivity from EEG sensors.

In Ecuador and several Latin American countries, the degree of contamination of urban soils by hazardous waste from petroleum derivatives is a matter of great concern, because according to the Environmental Protection Agency of the United States explains that a gallon of used lubricating oil contaminates a million gallons of water, the same that meets the needs of fifty people per year [1]. When oil is spilled on the land, it causes infertility in the soil because the used oil contains hydrocarbons that cause the death of the soil and transforms the vegetation into inert. Despite the existence of legislation regulating the use, storage, processing and treatment of waste, there are very few efficient methods that guarantee adequate environmental management of urban soil, either because they are technically complex or economically unfeasible. The objective of this research is to technically and economically evaluate the bioremediation of urban soils contaminated with petroleum derivatives in the city of Cuenca, Ecuador, using Pseudomonas bacteria. There are different methodologies and methods for soil remediation, the technique used for the recovery of soils contaminated with petroleum hydrocarbons in this research was bioremediation, through the application of an association of Pseudomonas bacteria obtained from the same soil, a technique called bio augmentation, and applied in three different concentrations and on the four soil samples obtained from the mechanics of the city of Cuenca. The Pseudomonas bacteria obtained, especially aeruginosa and fluorescence , demonstrated in the experimentation that they have the property of degrading hydrocarbons derived from petroleum, by feeding on carbon compounds in an exponential manner. For the calculation of the remediation cost-benefit, the value of the benefit acquired is divided by the remediation cost found. If the value is higher than the unit, the relation presents benefits; the relation obtained is 5.077, allowing to establish that the remediation process studied is economically viable. It is concluded that the method used is adequate and does not alter the soil with the introduction of foreign bacteria to it, in addition, the method studied serves for the remediation of soils contaminated with non-volatile petroleum hydrocarbons. The cost of its implementation is economically viable.

Fractal dimension (FD) has been revealed as a very useful tool in analyzing the changes in brain dynamics present in many neurological disorders. The fractal dimension index (FDI) is a measure of the spatiotemporal complexity of brain activations extracted from EEG signals induced by transcranial magnetic stimulation. In this study, we assess whether the FDI methodology can be also useful for analyzing resting state EEG signals, by characterizing the brain dynamic changes in different functional networks affected by schizophrenia, a mental disorder associated with dysfunction in the information flow dynamics in the spontaneous brain networks. We analyzed 31 resting-state EEG records of 150 s belonging to 20 healthy subjects (HC group) and 11 schizophrenia patients (SCZ group). Brain activations at each time sample were established by a thresholding process applied on the 15,002 sources modeled from the EEG signal. FDI was then computed individually in each resting-state functional network, averaging all the FDI values obtained using a sliding window of 1 s in the epoch. Compared to the HC group, significant lower values of FDI were obtained in the SCZ group for the auditory network ( p  < 0.05), the dorsal attention network ( p  < 0.05), and the salience network ( p  < 0.05). We found strong negative correlations ( p  < 0.01) between psychopathological scores and FDI in all resting-state networks analyzed, except the visual network. A receiver operating characteristic curve analysis also revealed that the FDI of the salience network performed very well as a potential feature for classifiers of schizophrenia, obtaining an area under curve value of 0.83. These results suggest that FDI is a promising method for assessing the complexity of the brain dynamics in different regions of interest, and from long resting-state EEG signals. Regarding the specific changes associated with schizophrenia in the dynamics of the spontaneous brain networks, FDI distinguished between patients and healthy subjects, and correlated to clinical variables.

Electroencephalograms (EEG) are one of the most commonly used measures to study brain functioning at a macroscopic level. The structure of the EEG time series is composed of many neural rhythms interacting at different spatiotemporal scales. This interaction is often named as cross frequency coupling, and consists of transient couplings between various parameters of different rhythms. This coupling has been hypothesized to be a basic mechanism involved in cognitive functions. There are several methods to measure cross frequency coupling between two rhythms but no single method has been selected as the gold standard. Current methods only serve to explore two rhythms at a time, are computationally demanding, and impose assumptions about the nature of the signal. Here we present a new approach based on Information Theory in which we can characterize the interaction of more than two rhythms in a given EEG time series. It estimates the mutual information of multiple rhythms (MIMR) extracted from the original signal. We tested this measure using simulated and real empirical data. We simulated signals composed of three frequencies and background noise. When the coupling between each frequency component was manipulated, we found a significant variation in the MIMR. In addition, we found that MIMR was sensitive to real EEG time series collected with open vs. closed eyes, and intra-cortical recordings from epileptic and non-epileptic signals registered at different regions of the brain. MIMR is presented as a tool to explore multiple rhythms, easy to compute and without a priori assumptions.

Dry forests are very fragile ecosystems as they are easily used as a source of subsistence products. In this sense, quantifying the carbon stock in these forests is of relevant importance for their conservation and to be able to quantify their participation as mitigation of the effects of climate change. Five 250 m2‐sample plots were established to estimate carbon stored in two pools for each of the plant formations identified (Dry Scrubland, DS; Dry Deciduous Forest, DDF; Dry Semideciduous Forest, DSF). The amount of carbon stored in soils was determined by analyzing the organic carbon randomly taken in each plot. Allometric equations were used to estimate the amount of carbon in above‐ground biomass, taking the total height (H) and the diameter at breast height (DBH) of trees whose DBH is equal to or greater than 5 cm. The total carbon stored in each plant formation was estimated by adding the amount of carbon in biomass and in soils, resulting in 60.30, 69.62, and 123.05 Mg of carbon per hectare for the DS, DDF, and DSF, respectively. The objective of the study was to estimate the carbon stored in tree biomass in two compartments of three vegetal formations in dry forest of the Ecuadorian coast.

Schizophrenia is characterized by widespread disruptions in neural connectivity and dynamic modulation. Traditional EEG analyses often rely on static or averaged measures, which may overlook the temporal evolution of neural complexity across cognitive demands. This study employed Higuchi Fractal Dimension, a non-linear measure of signal complexity, to examine the temporal dynamics of EEG activity across five cortical regions (central, frontal, occipital, parietal, and temporal lobes) during an attentional and a memory-based task in individuals diagnosed with schizophrenia and healthy controls. A permutation-based topographic analysis of variance revealed significant differences in neural complexity between tasks and groups. In the control group, results showed a consistent pattern of higher neural complexity during the attentional task across the different brain regions (except during a few moments in the temporal and occipital regions). This pattern of differentiation in complexity between the attentional and memory tasks reflects healthy individuals’ ability to dynamically modulate neural activity based on task-specific requirements. In contrast, the group of patients with schizophrenia exhibited inconsistent patterns of differences in complexity between tasks over time across all neural regions. That is, differences in complexity between tasks varies across time intervals, being sometimes higher in the attentional task and other times higher in the memory task (especially in the central, frontal, and temporal regions). This inconsistent pattern in patients can explain reduced task-specific modulation of EEG complexity in schizophrenia, and suggests a disruption in the modulation of neural activity on function of task demands. These findings underscore the importance of analyzing the temporal dynamics of EEG complexity to capture task-specific neural modulation.